# Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation

arXiv: https://arxiv.org/abs/1606.07830 · HTML: https://mlsanity.com/paper/1606.07830/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2016.

## Built on by (most cited first, 10 of 10)

- [FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images](https://mlsanity.com/paper/2104.01771/) (2021)
- [Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation](https://mlsanity.com/paper/2203.08964/) (2022)
- [Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction](https://mlsanity.com/paper/2105.14773/) (2021)
- [Hierarchical 3D Feature Learning for Pancreas Segmentation](https://mlsanity.com/paper/2109.01667/) (2021)
- [Evaluation of Preprocessing Techniques for U-Net Based Automated Liver Segmentation](https://mlsanity.com/paper/2103.14301/) (2021)
- [Multi-organ Segmentation Network with Adversarial Performance Validator](https://mlsanity.com/paper/2204.07850/) (2022)
- [External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data](https://mlsanity.com/paper/2201.00942/) (2022)
- [An overview of deep learning in medical imaging](https://mlsanity.com/paper/2202.08546/) (2022)
- [Pseudo-Label Guided Multi-Contrast Generalization for Non-Contrast Organ-Aware Segmentation](https://mlsanity.com/paper/2205.05898/) (2022)
- [Curriculum Knowledge Switching for Pancreas Segmentation](https://mlsanity.com/paper/2306.12651/) (2023)

## Abstract (arXiv)

Accurate automatic organ segmentation is an important yet challenging problem for medical image analysis. The pancreas is an abdominal organ with very high anatomical variability. This inhibits traditional segmentation methods from achieving high accuracies, especially compared to other organs such as the liver, heart or kidneys. In this paper, we present a holistic learning approach that integrates semantic mid-level cues of deeply-learned organ interior and boundary maps via robust spatial aggregation using random forest. Our method generates boundary preserving pixel-wise class labels for pancreas segmentation. Quantitative evaluation is performed on CT scans of 82 patients in 4-fold cross-validation. We achieve a (mean ± std. dev.) Dice Similarity Coefficient of 78.01% ± 8.2% in testing which significantly outperforms the previous state-of-the-art approach of 71.8% ± 10.7% under the same evaluation criterion.